Alternative lenders are coming to the forefront of the market as governmental regulatory changes and the increasingly tech-savvy market has demanded innovation.

But their ascendence has happened because of an even more important reason: The old model of loan evaluation is unequivocally broken.

Lending data analytics innovators joined VentureBeat analyst Evan Schuman to talk about how lenders can break out of old, broken underwriting models to make more profitable credit decisions using predictive modeling, analytics, and new ways to track down and incorporate alternate, crucial sources of customer data.

Outdated lending models

Traditional banks are commonly using outdated or misleading information and making customers wait weeks for a decision — and those decisions are often wrong.

The challenge is to fill in the gaps with alternative data sources. Ascend, which recently raised 1.5 million in financing, leverages a proprietary Adaptive Risk Pricing process powered by a broad array of real-time data that goes far beyond just FICA.

But, says Krishna Venkatraman, SVP of Data and Analytics at small-business loan provider OnDeck, “Resistance to new sources of data is a legacy of incumbency. Adoption of innovation that might disrupt your existing business model is always something that’s fraught.”

More and better data is a huge advantage

Alternative lenders have an advantage in how they build more holistic profiles of their applicants, with more far more essential data at the point of decision.

“More data gives you a better perspective, a very different view of the borrower,” says Crawford. The driving question, he says, is how to get a better, more automated, more robust view of the applicant’s capacity.

As innovative as the new models of customer evaluation are, says Terrence McKeown, practice manager, credit analytics at Envestnet | Yodlee, “It really goes back to the fundamentals of lending. Although we have a lot of the different data sources filling in the gaps of things we didn’t previously know, we have to make sure it’s still a good risk decision.”

However, Venkatraman warns, we’re still in the early days of this proliferation of data sources, which means they’re not yet standardized. “Part of the challenge is how do you gain that jungle of data and information, so that you can bring it in and systematically extract signals that can then inform your credit decisions,” he says.

It’s the ability to understand businesses in the context that they operate, and to allow the data to speak for itself that’s going to be a big differentiator for alternative lenders, and it will ultimately lead to better outcomes for businesses themselves.

But as Crawford cautions, big data is or can be unstructured. When building new lending models, companies are faced with the challenge of understanding exactly what signals they need to make effective decisions.

“The danger with any statistical model is overfitting, leading to spurious correlations that fall apart once they’re examined closely. Make sure any data you use is useful,” says Crawford. “Correlations should be explainable theoretically. It’s not always possible in big data machine models, but you should try — if only for regulatory reasons.”

It’s also essential, says, McKeown, to make sure your data is clean. “It’s great to have all this information,” he says, “but when you’re looking at this data, what’s the source of it, and was it able to be manipulated by the borrower in any way to influence the decision?”

The future of lending

Alternative lending solutions, Crawford says, are the future of the industry. “It’s not just that small upstarts are going to be more nimble about bringing things in,” he says. “It also goes to the testing of the credit models and their ability to quickly put things out into the marketplace and get feedback on it. I think that feedback loop is going to be really important as well as a driver on the credit modeling side.”

Some businesses will evolve faster than others, and slow adopters will follow as models are proven out and regulatory uncertainty goes away.

Perhaps big banks will never get there, with their current business structures. However, Crawford predicts, big banks may partner with smaller, more innovative companies whose business models and underwriting approaches have proven out.

For more on how new, innovative data approaches improve credit risk and drive profitability, check out the entire free webinar now.